Diverse clinical presentation in neurodevelopmental disorders (NDDs) leads to difficulty in matching individuals with effective treatments. Autism spectrum disorders (ASD) and attention deficit hyperactivity disorder (ADHD) are the two most prevalent neurodevelopmental disorders (NDDs), characterized by deficits in communication, social interactions, and behaviours. There is high within-diagnosis heterogeneity and striking overlap between diagnoses. The literature suggests that current diagnostic criteria do not align well with behaviour metrics. Therefore, identifying novel biomarkers underlying behaviour in NDDs may provide a reliable way to group individuals with similar behavioural phenotypes. This thesis examines how gut-immune biology is linked to clinical heterogeneity in children with NDDs. The first study used unsupervised machine learning to cluster typically developing (TD), ADHD, and ASD participants by their behaviour metrics in a diagnosis-agnostic approach. The results produced a six-cluster solution, five of which were a mix of all diagnostic categories. Further, gastrointestinal (GI) symptoms were mapped to the clusters, revealing a link between constipation, social communication deficits and restrictive-repetitive behaviours. The second study used hierarchical clustering to group TD and NDD participants based on a profile of gut and inflammatory markers. Participants clustered into two biotypes, both containing TD and NDD participants. Additionally, using regression analysis, novel markers were linked to anxiety. The third study evaluated the multisite biospecimen collection protocol of the Province of Ontario Neurodevelopmental Disorders (POND) Network. The final study used biospecimens collected from the POND network to phenotype peripheral blood mononuclear cells in TD and NDD participants. In NDD groups, monocyte and B cell activation markers were differentially expressed compared to TD. Overall, this thesis demonstrates that gut-immune mechanisms contribute to clinical heterogeneity in a subset of people and contribute to the search for biomarkers in NDDs. / Thesis / Doctor of Philosophy (PhD) / Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are diagnosed based on behavioural symptoms. However, symptoms can vary a lot from person to person, and some symptoms are shared between ASD and ADHD. Understanding the biological reasons for symptom differences between people can help pinpoint treatments which work best for an individual. This thesis looks at the role of the gut and immune system in ASD and ADHD. Blood samples and behaviour questionnaires were collected to study how immune cells, inflammation, and intestinal permeability shape behaviour symptoms. The results show that diagnosis is not the most accurate way to group people. Anxiety symptoms were different when people were grouped based on their inflammation levels. Also, specific immune cells appear to work differently in people with ASD and ADHD. These findings clarify some of the biology that affects behavioural symptoms in ASD and ADHD.

Obesity is a common disease among all ages that has threatened human health and has become a global concern. Gut microbiota can affect human metabolism and thus may modulate obesity. Certain mixes of gut microbiota can protect the host to be healthy or predispose the host to obesity. Modern next-generation sequencing technique allows accessing huge amount of genetic information underlying microbiota and thus provides new insights into the functionality of these micro-organisms and their interactions with the host. Multiple previous studies have demonstrated that the microbiome might contribute to obesity by increasing dietary energy harvest, promoting fat deposition and triggering systemic inflammation. However, these researches are either based on lab cultivation studies or basic statistical analysis. In order to further explore how gut microbiota affect obesity, this thesis utilize a series of machine learning methods to analyze large amount of metagenomics data from human gut microbiome. The publicly available HMP (Human Microbiome Project) metagenomic sequencing data, contain microbiome data for healthy adults, including overweight and obese individuals, were used for this study. HMP gut data were organized based on two different feature definitions: taxonomic information and metabolic reconstruction information. Several widely used classification algorithms: namely Naive Bayes, Random Forest, SVM and elastic net logistic regression were applied to predict healthy or obese status of the subjects based on the cross-validation accuracy. Furthermore, the corresponding feature selection algorithms were used to identify signature features in each dataset that lead to the differences between healthy and obese samples. The results showed that these algorithms perform poorly on taxonomic data than metabolic pathway data though lots of selected taxa are still supported by literature. Among all the combinations between different algorithms and data, elastic net logistic regression has the best cross-validation performance and thus becomes the best model. In this model, several important features are found and some of these are consistent with the previous studies. Rerunning classifiers by using features selected by elastic net logistic regression again further improved the performance of the classifiers. On the other hand, this study uncovered some new features that haven't been supported by previous studies. The new features could also be the potential target to distinguish obese and healthy subjects. The present thesis work compares the strengths and weaknesses of different machine learning techniques with different types of features originating from the same metagenomics data. The features selected by these models could provide a deep understanding of the metabolic mechanisms of micro-organisms. It is therefore worth to comprehensively understand the differences of gut microbiota between healthy and obese subjects, and particularly how gut microbiome affects obesity.

Using a novel, adipose-liver, two-organ, human-on-a-chip system, the metabolic disease non-alcoholic fatty liver disease was modeled. This model was then used to test the effects of the gut microbiome on NAFLD progression. Two products of the gut microbiome, Trimethylamine-n-oxide and butyrate, were selected as representatives of potentially harmful and potentially beneficial compounds. A dose response, adipocyte and hepatocyte monocultures controls, and HoaC systems were run for 14 days. Through this experimentation, it was found that a dysbiosis of the gut microbiome could be influencing NAFLD progression. Additionally, further development and discovery regarding adipose-liver systems was added to the ongoing conversation of HoaC systems and their usages.

Metagenomics is a cultivation-independent approach for obtaining the genomic composition of microbial communities. Microbial communities are ubiquitous in nature. Microbes which are associated with the human body play important roles in human health and disease. These roles span from protecting us against infections from other bacteria, to being the causes of these diseases. A deeper understanding of these communities and how they function inside our bodies allows for advancements in treatments and preventions for these diseases. Recent developments in metagenomics have been driven by the emergence of Next-Generation Sequencing technologies and Third-Generation Sequencing technologies that have enabled cost-effective DNA sequencing and the generation of large volumes of genomic data. These technologies have allowed for the introduction of hybrid DNA assembly techniques to recover the genomes of the constituent microbes. While Next-Generation Sequencing technologies use paired-end sequencing reads from DNA fragments into short reads and have a relatively lower error rate, Third-Generation Sequencing technologies use much longer DNA fragments to generate longer reads, bringing contigs together for larger scaffolds with a higher error rate. Hybrid assemblers leverage both short and long read sequencing technologies and can be a critical step in the advancements of metagenomics, combining these technologies to allow for longer assemblies of DNA with lower error rates. We evaluate the strengths and weaknesses of the hybrid assembly framework using several state-of-the-art assemblers and simulated human microbiome datasets. Our work provides insights into metagenomic assembly and genome recovery, an important step towards a deeper understanding of the microbial communities that influence our well-being.

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